Biology subjects

Oricchio, E.

Publications and source records attributed to Oricchio, E..

3 recordsLinked to original sources

bioRxiv2025

Mitotic slippage causes nuclear instability in polyploid cells

Whole-genome duplication (WGD), leading to polyploidy can arise in physiological and pathological contexts1-5. WGD can occur via non-canonical cell cycles such as mitotic slippage, cytokinesis failure or endoreplication1,3. Whether the routes to WGD influence the behaviour of the resulting polyploid cells remains unclear. Here, we compared these routes under both physiological and non-physiological conditions. Remarkably, only mitotic slippage led to widespread nuclear abnormalities defined by highly variable nuclear deformations that we termed nuclear instability. Mechanistically, we found that these nuclei were softer - due to high levels of histone 3 phosphorylation in G1 altering chromatin compaction - and thus more vulnerable to microtubule-driven deformations. The resulting nuclear instability leads to local nuclear reorganisation and changes in 3D genome organisation impacting ultimately gene expression. Importantly, we observed similar nuclear instability in megakaryocytes, which are physiological polyploid cells that we show here to be generated by mitotic slippage, providing a molecular mechanism for their atypical nuclear architecture6,7. In striking contrast, nuclear shape was stable in different physiological polyploid cells generated by cytokinesis failure and endoreplication. Overall, our findings highlight that the route towards WGD matters and that mitotic slippage uniquely destabilizes nuclear architecture, with implications for both physiology and disease.

cell biology↗

bioRxiv2024

Patient-derived lymphomoids preserve the tumor architecture and allow to assess response to therapies in lymphoma

The efficacy of anti-cancer therapies depends on the genomic composition of the tumor, its microenvironment, spatial organization, and intra-tumor heterogeneity. B cell lymphomas are a heterogeneous group of tumors emerging from B cells at different stages of differentiation and exhibiting tumor-specific interactions with the tumor microenvironment. Thus, to measure response to therapy in lymphoma, it is critical to preserve the tumor composition and functional interactions among immune cells. Here, we developed a platform to maintain small fragments of human lymphoma tissue in culture for several days and use them to test response to therapies. We collected 25 patient samples representative of different lymphoma subtypes and established ex vivo tissue fragments that retained histological, cellular, and molecular characteristics of the original tissue, here referred to as lymphomoids. Using lymphomoids, we tested sensitivity to several clinically approved small molecule inhibitors in parallel and examined tissue remodeling upon treatment. Importantly, when this information was available, we showed that sensitivity to therapy observed in lymphomoids was consistent with patients response in the clinic. Lymphomoids are an innovative tool to assess treatment efficacy in clinically relevant contexts and could be used to uncover novel aspects of lymphoma biology.

cancer biology↗

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗